The Clinical De-Identification model is designed to recognize and anonymize PHI in Portuguese-language clinical notes. It employs state-of-the-art natural language processing techniques to detect sensitive information such as patient names, addresses, medical record numbers, and other identifiers. Once identified, the PHI is effectively masked or obfuscated, rendering the text safe for broader use while maintaining its informational integrity.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
Process up to 8M chars per hour in real-time and 20 M chars per hour in batch mode.
**Key Features:**
- The model is tuned to identify wide range of PHI elements in medical texts, ensuring comprehensive de-identification.
- The process aligns with GDPR and other healthcare privacy regulations, aiding in legal compliance and data protection.
- Ideal for research, analytics, and training purposes, this model enables the safe utilization of medical texts without compromising patient privacy.
This model is a useful asset in the healthcare and research sectors, where the protection of patient privacy is paramount. It allows for the ethical and legal use of valuable medical data, promoting research and analysis while upholding the highest standards of data privacy and security.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay by the hour for each running instance, with no separate software license fee listed here. Pricing is organized by two inference modes and by instance type. Batch mode covers larger 2xlarge instances across general-purpose, compute-optimized, and memory-optimized families. Real-time mode covers xlarge instances across the same families. Within each mode, your hourly rate changes based on the instance you choose, so you scale cost by selecting a size and hardware class that fit your workload. You run as many hours as needed and pay only for host time used.
Top-of-mind questions for buyers
What counts as one billable host-hour, and am I charged when the instance is stopped?
One host-hour is one running instance for one hour of the chosen type and mode. Charges meter running time only. A fully stopped instance stops accruing software charges, though underlying AWS storage fees may still apply while resources remain provisioned.
What is the difference between batch mode and real-time mode when choosing an instance?
Batch mode runs on 2xlarge instances and processes grouped documents together, which suits large jobs run periodically. Real-time mode runs on xlarge instances and serves single requests on demand, which suits live de-identification. You pick the mode and pay that instance's hourly rate while it runs.
Is there a limit on documents processed per host-hour, or does volume add separate charges?
No. There is no limit on the number of characters, words, or documents processed. Your bill is driven only by instance-hours, not document count. You run as many documents as your chosen instance handles within each paid hour, with no per-document or per-token fee.
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An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Real-time inference
Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .
Batch transform
Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .
Version release notes
New Version for JSL libs 6.4.0
Additional details
Inputs
Outputs
Usage instructions
Sample notebooks
Inputs
Summary
Input Format
To use the model, you need to provide input in one of the following supported formats:
JSON Format
Provide input as JSON. We support two variations within this format:
Array of Text Documents:
Use an array containing multiple text documents. Each element represents a separate text document.
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